Frequent Patterns Mining Based On Semi-structured Data Model

With the development of information technology, Data Mining has been paid attention extensively, which stimulates more and more people who work in the field to research deeply into it. As we know, Data Mining has a large research scope. Association rules data mining is one of important research subject in it. Deeply researching into the subject has most important values not only on theoretics but also on applications. In the research of association rules data mining, the algorithms research is its key part for mining association rules. Many highly efficient algorithms in the field has been put forward for mining association rules so far.Frequent itemset mining plays a crucial role in many data mining applications. It occurs in the discovery of association rules, strong rules, correlations, multidimensional patterns, and many other important discovery tasks. Frequent itemset mining dominates the time complexity of the discovery algorithms.First of all, this thesis made an analysis and conclusion of the current situation and development of the Data Mining Technique. Then it gave a comprehensive explanation of Association rules' basic knowledge, especially in the field of Frequent Patterns Mining, on which many researchers have done a great number of studies and made remarkable achievements. This thesis concentrated on the ideology and realization of the classic Apriori algorithm and FP-growth algorithm, and made a comparison between these two algorithms on their performance.Secondly, under the circumstances of semi-structured data, the wide use of each item element of Frequent Patterns Mining, such as semi-structured data, Semi-structured data Model, XML technique, etc. generated a large number of achievements in this area.Finally, according to the analysis of the currently largest frequent itemsets and its shortage, the thesis brought forward an improved algorithm of mining the largest frequent itemsets.